Differential DNA Methylation in Purified Human Cord and Peripheral Blood: Biomarkers of Prenatal Smoking and Allergy in Children
Bibliographic record
Abstract
Adjusting for nonresponse, 7.7% of Canadian adults suffer from allergies. On a broader scale, Allergic rhinitis affects approximately 10 – 25% of the world population. The study analyzed data (n=185) from the Kingston Allergy Birth Cohort study, a prospective birth cohort that has recruited over 300 pregnant women to date. A skin prick tests was administered to each child to quantify the phenotype, allergies, on a binary scale. Surveys filled out by the mothers revealed that 28% engaged in prenatal smoking, the highest rate in Ontario. Methylation in the human genome is known to be associated with development and disease. The study defines the methylome as the set of nucleic acid methylation modifications in a subject’s genome. With the Infinium MethylationEPIC BeadChip, the study collected DNA samples and examined over 850,000 methylation sites quantitatively across the genome at single-nucleotideresolution, to investigate the effects of allergies, and maternal smoking, on the methylome. Pre–processing steps including quality control, normalisation, data exploration, non-specific filtering, and statistical testing for probe-wise differential methylation was applied. After pre-processing and outlier removal, umbilical cord blood (n=50) and peripheral blood (n=70) was analyzed from the subjects. The study found differentially methylated sites that represent potential biomarkers that could be predictive of future atopic disease in childhood. These potential biomarkers may also serve as a more accurate method, than surveys filled out by the mothers which are susceptible to patient bias, in determining if a child was subject to prenatal smoking.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".